"""
Advanced Distributed Training System for Toxo

This module implements production-grade distributed training capabilities including:
- Federated Learning with secure aggregation
- Auto-scaling worker clusters
- Intelligent workload distribution and load balancing
- Fault tolerance with automatic recovery
- Real-time coordination and synchronization
- GPU/TPU resource management
- Hierarchical parameter servers
- Advanced optimization algorithms (AllReduce, Parameter Server, etc.)
"""

import asyncio
import numpy as np
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
import ray
import redis
import consul
import etcd3
import json
import uuid
import hashlib
import time
import pickle
from typing import Dict, Any, List, Optional, Union, Callable, Tuple, Set
from dataclasses import dataclass, field, asdict
from datetime import datetime, timedelta
from enum import Enum
from pathlib import Path
from collections import defaultdict, deque
import threading
import multiprocessing as mp
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, Future
import logging
import socket
import psutil
import GPUtil
from queue import PriorityQueue
import grpc
from grpc import aio as aio_grpc

# ML libraries
from sklearn.cluster import KMeans
import networkx as nx

# Cryptography for secure aggregation
from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import base64

# Toxo imports
from ..utils.logger import get_logger
from ..utils.exceptions import DistributedTrainingError, ResourceError
from ..core.config import ToxoConfig
from .monitoring import MetricsCollector


class TrainingStrategy(Enum):
    """Training strategies for distributed training."""
    FEDERATED_LEARNING = "federated_learning"
    PARAMETER_SERVER = "parameter_server"
    ALL_REDUCE = "all_reduce"
    PIPELINE_PARALLEL = "pipeline_parallel"
    MODEL_PARALLEL = "model_parallel"
    HYBRID = "hybrid"
    ASYNCHRONOUS_SGD = "async_sgd"
    SYNCHRONOUS_SGD = "sync_sgd"


class WorkerType(Enum):
    """Types of workers in the distributed system."""
    TRAINER = "trainer"
    PARAMETER_SERVER = "parameter_server"
    COORDINATOR = "coordinator"
    AGGREGATOR = "aggregator"
    VALIDATOR = "validator"
    DATA_PROCESSOR = "data_processor"


class ResourceType(Enum):
    """Types of computational resources."""
    CPU = "cpu"
    GPU = "gpu"
    TPU = "tpu"
    MEMORY = "memory"
    STORAGE = "storage"
    NETWORK = "network"


@dataclass
class TrainingJob:
    """Represents a distributed training job."""
    job_id: str
    job_name: str
    strategy: TrainingStrategy
    model_config: Dict[str, Any]
    training_config: Dict[str, Any]
    data_config: Dict[str, Any]
    resource_requirements: Dict[ResourceType, int]
    priority: int = 1
    estimated_duration: Optional[int] = None  # seconds
    dependencies: List[str] = field(default_factory=list)
    created_at: datetime = field(default_factory=datetime.now)
    status: str = "pending"
    progress: float = 0.0
    error_message: Optional[str] = None
    result_location: Optional[str] = None
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class WorkerNode:
    """Represents a worker node in the distributed system."""
    node_id: str
    host: str
    port: int
    worker_type: WorkerType
    capabilities: Set[ResourceType]
    current_load: float = 0.0
    max_capacity: Dict[ResourceType, float] = field(default_factory=dict)
    current_usage: Dict[ResourceType, float] = field(default_factory=dict)
    status: str = "idle"  # idle, busy, offline, error
    last_heartbeat: datetime = field(default_factory=datetime.now)
    performance_score: float = 1.0
    assigned_jobs: List[str] = field(default_factory=list)
    gpu_info: Dict[str, Any] = field(default_factory=dict)
    metadata: Dict[str, Any] = field(default_factory=dict)
    
    def get_availability_score(self) -> float:
        """Calculate availability score based on current load and capacity."""
        if self.status != "idle":
            return 0.0
        
        # Calculate weighted availability
        total_capacity = sum(self.max_capacity.values())
        total_usage = sum(self.current_usage.values())
        
        if total_capacity == 0:
            return 0.0
        
        availability = 1.0 - (total_usage / total_capacity)
        return availability * self.performance_score


@dataclass
class TrainingRound:
    """Represents a round in federated learning."""
    round_id: str
    job_id: str
    round_number: int
    participants: List[str]
    global_model_hash: str
    aggregation_strategy: str
    started_at: datetime = field(default_factory=datetime.now)
    completed_at: Optional[datetime] = None
    success: bool = False
    convergence_metrics: Dict[str, float] = field(default_factory=dict)


class SecureAggregator:
    """
    Secure aggregation for federated learning.
    Implements differential privacy and secure multi-party computation.
    """
    
    def __init__(self, privacy_budget: float = 1.0, noise_multiplier: float = 1.0):
        self.privacy_budget = privacy_budget
        self.noise_multiplier = noise_multiplier
        self.logger = get_logger(f"{__name__}.SecureAggregator")
        
        # Initialize encryption
        self._init_encryption()
    
    def _init_encryption(self):
        """Initialize encryption for secure aggregation."""
        # Generate key for symmetric encryption
        password = b"toxo_secure_aggregation"  # In production, use proper key management
        salt = b"toxo_salt_12345"  # In production, use random salt
        kdf = PBKDF2HMAC(
            algorithm=hashes.SHA256(),
            length=32,
            salt=salt,
            iterations=100000,
        )
        key = base64.urlsafe_b64encode(kdf.derive(password))
        self.cipher = Fernet(key)
    
    async def aggregate_models(
        self,
        encrypted_models: List[bytes],
        weights: Optional[List[float]] = None
    ) -> np.ndarray:
        """
        Aggregate encrypted model updates securely.
        
        Args:
            encrypted_models: List of encrypted model parameters
            weights: Optional weights for weighted aggregation
            
        Returns:
            Aggregated model parameters
        """
        try:
            # Decrypt models
            decrypted_models = []
            for encrypted_model in encrypted_models:
                try:
                    decrypted_data = self.cipher.decrypt(encrypted_model)
                    model_params = pickle.loads(decrypted_data)
                    decrypted_models.append(model_params)
                except Exception as e:
                    self.logger.warning(f"Failed to decrypt model: {e}")
                    continue
            
            if not decrypted_models:
                raise DistributedTrainingError("No valid models to aggregate")
            
            # Apply differential privacy noise
            aggregated = await self._dp_aggregate(decrypted_models, weights)
            
            return aggregated
            
        except Exception as e:
            self.logger.error(f"Secure aggregation failed: {e}")
            raise DistributedTrainingError(f"Aggregation failed: {e}")
    
    async def _dp_aggregate(
        self,
        models: List[np.ndarray],
        weights: Optional[List[float]] = None
    ) -> np.ndarray:
        """Apply differential privacy to aggregation."""
        if weights is None:
            weights = [1.0 / len(models)] * len(models)
        
        # Weighted average
        aggregated = np.zeros_like(models[0])
        for model, weight in zip(models, weights):
            aggregated += weight * model
        
        # Add differential privacy noise
        noise_scale = self.noise_multiplier / self.privacy_budget
        noise = np.random.normal(0, noise_scale, aggregated.shape)
        aggregated += noise
        
        return aggregated
    
    def encrypt_model(self, model_params: np.ndarray) -> bytes:
        """Encrypt model parameters."""
        serialized = pickle.dumps(model_params)
        return self.cipher.encrypt(serialized)


class ResourceManager:
    """
    Advanced resource management for distributed training.
    Handles GPU/TPU allocation, auto-scaling, and load balancing.
    """
    
    def __init__(self):
        self.logger = get_logger(f"{__name__}.ResourceManager")
        self.nodes: Dict[str, WorkerNode] = {}
        self.resource_locks = defaultdict(threading.Lock)
        self.allocation_history = deque(maxlen=1000)
        
        # Auto-scaling parameters
        self.min_nodes = 1
        self.max_nodes = 100
        self.scale_up_threshold = 0.8
        self.scale_down_threshold = 0.3
        self.scale_check_interval = 60  # seconds
        
        # Performance tracking
        self.node_performance = defaultdict(list)
        
    async def register_node(self, node: WorkerNode) -> bool:
        """Register a new worker node."""
        try:
            # Update GPU information
            if ResourceType.GPU in node.capabilities:
                node.gpu_info = await self._get_gpu_info()
            
            # Calculate initial performance score
            node.performance_score = await self._calculate_performance_score(node)
            
            self.nodes[node.node_id] = node
            self.logger.info(f"Registered node {node.node_id} with capabilities {node.capabilities}")
            
            return True
            
        except Exception as e:
            self.logger.error(f"Failed to register node {node.node_id}: {e}")
            return False
    
    async def _get_gpu_info(self) -> Dict[str, Any]:
        """Get GPU information for the current node."""
        try:
            gpus = GPUtil.getGPUs()
            gpu_info = []
            
            for gpu in gpus:
                gpu_info.append({
                    "id": gpu.id,
                    "name": gpu.name,
                    "memory_total": gpu.memoryTotal,
                    "memory_used": gpu.memoryUsed,
                    "memory_free": gpu.memoryFree,
                    "load": gpu.load,
                    "temperature": gpu.temperature
                })
            
            return {"gpus": gpu_info, "count": len(gpus)}
            
        except Exception as e:
            self.logger.warning(f"Failed to get GPU info: {e}")
            return {"gpus": [], "count": 0}
    
    async def _calculate_performance_score(self, node: WorkerNode) -> float:
        """Calculate performance score for a node."""
        base_score = 1.0
        
        # Factor in GPU capabilities
        if ResourceType.GPU in node.capabilities:
            gpu_count = node.gpu_info.get("count", 0)
            base_score *= (1.0 + 0.5 * gpu_count)
        
        # Factor in memory capacity
        memory_capacity = node.max_capacity.get(ResourceType.MEMORY, 0)
        base_score *= (1.0 + memory_capacity / 32.0)  # Normalize by 32GB
        
        # Factor in historical performance
        if node.node_id in self.node_performance:
            recent_performance = self.node_performance[node.node_id][-10:]
            avg_performance = np.mean(recent_performance) if recent_performance else 1.0
            base_score *= avg_performance
        
        return min(base_score, 5.0)  # Cap at 5x
    
    async def allocate_resources(
        self,
        job: TrainingJob,
        strategy: Optional[str] = "best_fit"
    ) -> Dict[str, WorkerNode]:
        """
        Allocate resources for a training job.
        
        Args:
            job: Training job requiring resources
            strategy: Allocation strategy
            
        Returns:
            Dictionary of allocated nodes
        """
        try:
            if strategy == "best_fit":
                return await self._best_fit_allocation(job)
            elif strategy == "load_balance":
                return await self._load_balanced_allocation(job)
            elif strategy == "performance_optimized":
                return await self._performance_optimized_allocation(job)
            else:
                return await self._best_fit_allocation(job)
                
        except Exception as e:
            self.logger.error(f"Resource allocation failed for job {job.job_id}: {e}")
            raise ResourceError(f"Failed to allocate resources: {e}")
    
    async def _best_fit_allocation(self, job: TrainingJob) -> Dict[str, WorkerNode]:
        """Best-fit resource allocation algorithm."""
        allocated_nodes = {}
        required_resources = job.resource_requirements
        
        # Sort nodes by availability score
        available_nodes = [
            node for node in self.nodes.values()
            if node.status == "idle" and self._can_satisfy_requirements(node, required_resources)
        ]
        
        available_nodes.sort(key=lambda n: n.get_availability_score(), reverse=True)
        
        # Allocate nodes greedily
        remaining_requirements = dict(required_resources)
        
        for node in available_nodes:
            if all(req <= 0 for req in remaining_requirements.values()):
                break
            
            # Calculate how much this node can contribute
            contribution = {}
            for resource_type, required_amount in remaining_requirements.items():
                if required_amount <= 0:
                    contribution[resource_type] = 0
                    continue
                
                available_amount = node.max_capacity.get(resource_type, 0) - node.current_usage.get(resource_type, 0)
                contribution[resource_type] = min(required_amount, available_amount)
            
            # If node can contribute, allocate it
            if any(contrib > 0 for contrib in contribution.values()):
                allocated_nodes[node.node_id] = node
                
                # Update remaining requirements
                for resource_type, contrib in contribution.items():
                    remaining_requirements[resource_type] -= contrib
                
                # Update node usage
                for resource_type, contrib in contribution.items():
                    node.current_usage[resource_type] = node.current_usage.get(resource_type, 0) + contrib
                
                node.status = "busy"
                node.assigned_jobs.append(job.job_id)
        
        # Check if all requirements are satisfied
        if any(req > 0 for req in remaining_requirements.values()):
            # Rollback allocations
            for node in allocated_nodes.values():
                self._release_node_resources(node, job.job_id)
            
            raise ResourceError(f"Insufficient resources for job {job.job_id}")
        
        # Record allocation
        self.allocation_history.append({
            "job_id": job.job_id,
            "allocated_nodes": list(allocated_nodes.keys()),
            "timestamp": datetime.now(),
            "requirements": dict(required_resources)
        })
        
        return allocated_nodes
    
    async def _load_balanced_allocation(self, job: TrainingJob) -> Dict[str, WorkerNode]:
        """Load-balanced allocation strategy."""
        # Find nodes with lowest current load
        available_nodes = [
            node for node in self.nodes.values()
            if node.status == "idle"
        ]
        
        # Sort by current load
        available_nodes.sort(key=lambda n: n.current_load)
        
        # Distribute resources evenly
        allocated_nodes = {}
        total_required = sum(job.resource_requirements.values())
        
        for i, node in enumerate(available_nodes):
            if i >= total_required:
                break
            
            allocated_nodes[node.node_id] = node
            node.status = "busy"
            node.assigned_jobs.append(job.job_id)
            node.current_load += 1.0 / len(available_nodes)
        
        return allocated_nodes
    
    async def _performance_optimized_allocation(self, job: TrainingJob) -> Dict[str, WorkerNode]:
        """Performance-optimized allocation strategy."""
        # Sort nodes by performance score
        available_nodes = [
            node for node in self.nodes.values()
            if node.status == "idle"
        ]
        
        available_nodes.sort(key=lambda n: n.performance_score, reverse=True)
        
        # Allocate best performing nodes first
        allocated_nodes = {}
        required_count = max(job.resource_requirements.values())
        
        for i, node in enumerate(available_nodes[:required_count]):
            allocated_nodes[node.node_id] = node
            node.status = "busy"
            node.assigned_jobs.append(job.job_id)
        
        return allocated_nodes
    
    def _can_satisfy_requirements(
        self,
        node: WorkerNode,
        requirements: Dict[ResourceType, int]
    ) -> bool:
        """Check if a node can satisfy resource requirements."""
        for resource_type, required_amount in requirements.items():
            available_amount = node.max_capacity.get(resource_type, 0) - node.current_usage.get(resource_type, 0)
            if available_amount < required_amount:
                return False
        return True
    
    def _release_node_resources(self, node: WorkerNode, job_id: str):
        """Release resources from a node."""
        if job_id in node.assigned_jobs:
            node.assigned_jobs.remove(job_id)
        
        if not node.assigned_jobs:
            node.status = "idle"
            node.current_usage.clear()
            node.current_load = 0.0
    
    async def release_resources(self, job_id: str, allocated_nodes: Dict[str, WorkerNode]):
        """Release allocated resources for a job."""
        for node in allocated_nodes.values():
            self._release_node_resources(node, job_id)
        
        self.logger.info(f"Released resources for job {job_id}")
    
    async def auto_scale(self) -> Dict[str, Any]:
        """Perform auto-scaling based on current load."""
        current_load = self._calculate_cluster_load()
        scaling_action = None
        
        if current_load > self.scale_up_threshold and len(self.nodes) < self.max_nodes:
            scaling_action = await self._scale_up()
        elif current_load < self.scale_down_threshold and len(self.nodes) > self.min_nodes:
            scaling_action = await self._scale_down()
        
        return {
            "current_load": current_load,
            "node_count": len(self.nodes),
            "scaling_action": scaling_action,
            "timestamp": datetime.now()
        }
    
    def _calculate_cluster_load(self) -> float:
        """Calculate overall cluster load."""
        if not self.nodes:
            return 0.0
        
        total_load = sum(node.current_load for node in self.nodes.values())
        return total_load / len(self.nodes)
    
    async def _scale_up(self) -> Dict[str, Any]:
        """Scale up the cluster by adding nodes."""
        # In a real implementation, this would interface with cloud providers
        # or container orchestrators to spin up new nodes
        self.logger.info("Scaling up cluster")
        return {"action": "scale_up", "requested_nodes": 1}
    
    async def _scale_down(self) -> Dict[str, Any]:
        """Scale down the cluster by removing idle nodes."""
        idle_nodes = [node for node in self.nodes.values() if node.status == "idle"]
        
        if idle_nodes:
            # Remove the node with lowest performance score
            node_to_remove = min(idle_nodes, key=lambda n: n.performance_score)
            del self.nodes[node_to_remove.node_id]
            self.logger.info(f"Scaled down cluster by removing node {node_to_remove.node_id}")
            return {"action": "scale_down", "removed_node": node_to_remove.node_id}
        
        return {"action": "scale_down", "result": "no_idle_nodes"}


class FederatedLearningCoordinator:
    """
    Coordinates federated learning across distributed nodes.
    Implements various FL algorithms and aggregation strategies.
    """
    
    def __init__(
        self,
        aggregation_strategy: str = "fedavg",
        min_participants: int = 2,
        max_participants: int = 100,
        convergence_threshold: float = 0.001
    ):
        self.aggregation_strategy = aggregation_strategy
        self.min_participants = min_participants
        self.max_participants = max_participants
        self.convergence_threshold = convergence_threshold
        self.logger = get_logger(f"{__name__}.FederatedCoordinator")
        
        # Training state
        self.active_jobs: Dict[str, TrainingJob] = {}
        self.training_rounds: Dict[str, List[TrainingRound]] = defaultdict(list)
        self.global_models: Dict[str, np.ndarray] = {}
        
        # Secure aggregation
        self.secure_aggregator = SecureAggregator()
        
        # Communication
        self.participant_connections: Dict[str, Any] = {}
    
    async def start_federated_training(
        self,
        job: TrainingJob,
        participants: List[WorkerNode]
    ) -> str:
        """
        Start a federated learning training job.
        
        Args:
            job: Training job configuration
            participants: List of participating nodes
            
        Returns:
            Training session ID
        """
        try:
            if len(participants) < self.min_participants:
                raise DistributedTrainingError(f"Insufficient participants: {len(participants)} < {self.min_participants}")
            
            # Initialize global model
            self.global_models[job.job_id] = await self._initialize_global_model(job)
            
            # Start training rounds
            self.active_jobs[job.job_id] = job
            
            # Begin first round
            first_round = await self._start_training_round(job.job_id, participants)
            
            self.logger.info(f"Started federated training job {job.job_id} with {len(participants)} participants")
            
            return first_round.round_id
            
        except Exception as e:
            self.logger.error(f"Failed to start federated training: {e}")
            raise DistributedTrainingError(f"Training start failed: {e}")
    
    async def _initialize_global_model(self, job: TrainingJob) -> np.ndarray:
        """Initialize the global model."""
        model_config = job.model_config
        
        # Create initial model parameters
        # This is a simplified example - in practice, this would depend on the model architecture
        param_size = model_config.get("param_size", 1000)
        initial_model = np.random.normal(0, 0.1, param_size)
        
        return initial_model
    
    async def _start_training_round(
        self,
        job_id: str,
        participants: List[WorkerNode]
    ) -> TrainingRound:
        """Start a new training round."""
        round_number = len(self.training_rounds[job_id]) + 1
        round_id = f"{job_id}_round_{round_number}"
        
        # Create round
        training_round = TrainingRound(
            round_id=round_id,
            job_id=job_id,
            round_number=round_number,
            participants=[node.node_id for node in participants],
            global_model_hash=self._hash_model(self.global_models[job_id]),
            aggregation_strategy=self.aggregation_strategy
        )
        
        self.training_rounds[job_id].append(training_round)
        
        # Send global model to participants
        await self._distribute_global_model(job_id, participants)
        
        return training_round
    
    def _hash_model(self, model: np.ndarray) -> str:
        """Generate hash for model parameters."""
        model_bytes = model.tobytes()
        return hashlib.sha256(model_bytes).hexdigest()
    
    async def _distribute_global_model(
        self,
        job_id: str,
        participants: List[WorkerNode]
    ):
        """Distribute global model to participants."""
        global_model = self.global_models[job_id]
        encrypted_model = self.secure_aggregator.encrypt_model(global_model)
        
        # In a real implementation, this would send the model via network
        for participant in participants:
            self.logger.debug(f"Sending global model to participant {participant.node_id}")
            # await self._send_model_to_node(participant, encrypted_model)
    
    async def collect_local_updates(
        self,
        round_id: str,
        local_updates: Dict[str, bytes]
    ) -> bool:
        """
        Collect local model updates from participants.
        
        Args:
            round_id: Training round ID
            local_updates: Dictionary of node_id -> encrypted_model_update
            
        Returns:
            True if aggregation successful
        """
        try:
            # Find the training round
            job_id = round_id.split("_round_")[0]
            current_round = None
            
            for round_obj in self.training_rounds[job_id]:
                if round_obj.round_id == round_id:
                    current_round = round_obj
                    break
            
            if not current_round:
                raise DistributedTrainingError(f"Training round {round_id} not found")
            
            # Aggregate updates
            encrypted_updates = list(local_updates.values())
            aggregated_model = await self.secure_aggregator.aggregate_models(encrypted_updates)
            
            # Update global model
            self.global_models[job_id] = aggregated_model
            
            # Check convergence
            convergence_metrics = await self._calculate_convergence_metrics(job_id)
            current_round.convergence_metrics = convergence_metrics
            current_round.completed_at = datetime.now()
            current_round.success = True
            
            # Check if training should continue
            if convergence_metrics.get("loss_improvement", float('inf')) < self.convergence_threshold:
                await self._complete_training(job_id)
            else:
                # Start next round
                participants = [self._get_node_by_id(node_id) for node_id in current_round.participants]
                await self._start_training_round(job_id, participants)
            
            return True
            
        except Exception as e:
            self.logger.error(f"Failed to collect local updates: {e}")
            return False
    
    async def _calculate_convergence_metrics(self, job_id: str) -> Dict[str, float]:
        """Calculate convergence metrics for the current model."""
        # Simplified convergence calculation
        # In practice, this would involve validation on held-out data
        
        rounds = self.training_rounds[job_id]
        if len(rounds) < 2:
            return {"loss_improvement": float('inf')}
        
        # Simulate loss improvement calculation
        loss_improvement = np.random.exponential(0.1)  # Simulated decreasing loss
        
        return {
            "loss_improvement": loss_improvement,
            "round_number": len(rounds),
            "participants": len(rounds[-1].participants)
        }
    
    def _get_node_by_id(self, node_id: str) -> WorkerNode:
        """Get worker node by ID."""
        # This would be implemented with proper node registry
        return WorkerNode(
            node_id=node_id,
            host="localhost",
            port=8000,
            worker_type=WorkerType.TRAINER,
            capabilities={ResourceType.CPU}
        )
    
    async def _complete_training(self, job_id: str):
        """Complete the federated training job."""
        job = self.active_jobs[job_id]
        job.status = "completed"
        job.progress = 1.0
        
        # Save final model
        final_model = self.global_models[job_id]
        model_path = f"models/{job_id}_final_model.npy"
        np.save(model_path, final_model)
        job.result_location = model_path
        
        self.logger.info(f"Completed federated training job {job_id}")


class AdvancedDistributedTrainingManager:
    """
    Main manager for advanced distributed training in Toxo.
    Orchestrates all components and provides unified API.
    """
    
    def __init__(self, config: Optional[ToxoConfig] = None):
        self.config = config or ToxoConfig()
        self.logger = get_logger(__name__)
        
        # Core components
        self.resource_manager = ResourceManager()
        self.federated_coordinator = FederatedLearningCoordinator()
        self.metrics_collector = MetricsCollector()
        
        # Job management
        self.active_jobs: Dict[str, TrainingJob] = {}
        self.job_queue = PriorityQueue()
        self.job_history: List[TrainingJob] = []
        
        # Coordination
        self.coordinator_thread: Optional[threading.Thread] = None
        self.running = False
        
        # Ray cluster (optional)
        self.ray_initialized = False
        
        self.logger.info("Advanced Distributed Training Manager initialized")
    
    async def initialize_cluster(
        self,
        cluster_config: Optional[Dict[str, Any]] = None
    ) -> Dict[str, Any]:
        """
        Initialize the distributed training cluster.
        
        Args:
            cluster_config: Cluster configuration
            
        Returns:
            Initialization status
        """
        try:
            cluster_config = cluster_config or {}
            
            # Initialize Ray if requested
            if cluster_config.get("use_ray", False):
                await self._initialize_ray_cluster(cluster_config)
            
            # Register local node
            local_node = await self._create_local_node()
            await self.resource_manager.register_node(local_node)
            
            # Start coordination thread
            self.running = True
            self.coordinator_thread = threading.Thread(target=self._coordination_loop)
            self.coordinator_thread.start()
            
            # Start auto-scaling monitoring
            asyncio.create_task(self._auto_scaling_loop())
            
            return {
                "status": "initialized",
                "cluster_size": len(self.resource_manager.nodes),
                "ray_enabled": self.ray_initialized,
                "local_node_id": local_node.node_id
            }
            
        except Exception as e:
            self.logger.error(f"Failed to initialize cluster: {e}")
            raise DistributedTrainingError(f"Cluster initialization failed: {e}")
    
    async def _initialize_ray_cluster(self, cluster_config: Dict[str, Any]):
        """Initialize Ray cluster for distributed computing."""
        try:
            import ray
            
            ray_config = cluster_config.get("ray_config", {})
            
            if not ray.is_initialized():
                ray.init(**ray_config)
                self.ray_initialized = True
                self.logger.info("Ray cluster initialized")
            
        except ImportError:
            self.logger.warning("Ray not available, skipping Ray initialization")
        except Exception as e:
            self.logger.error(f"Failed to initialize Ray: {e}")
    
    async def _create_local_node(self) -> WorkerNode:
        """Create local worker node."""
        node_id = f"local_{socket.gethostname()}_{uuid.uuid4().hex[:8]}"
        
        # Detect capabilities
        capabilities = {ResourceType.CPU, ResourceType.MEMORY}
        
        # Check for GPU
        try:
            import GPUtil
            gpus = GPUtil.getGPUs()
            if gpus:
                capabilities.add(ResourceType.GPU)
        except:
            pass
        
        # Get system resources
        cpu_count = psutil.cpu_count()
        memory_gb = psutil.virtual_memory().total / (1024**3)
        
        max_capacity = {
            ResourceType.CPU: cpu_count,
            ResourceType.MEMORY: memory_gb
        }
        
        node = WorkerNode(
            node_id=node_id,
            host="localhost",
            port=8000,
            worker_type=WorkerType.TRAINER,
            capabilities=capabilities,
            max_capacity=max_capacity
        )
        
        return node
    
    def _coordination_loop(self):
        """Main coordination loop for job scheduling."""
        while self.running:
            try:
                # Process pending jobs
                self._process_job_queue()
                
                # Update node heartbeats
                self._update_node_heartbeats()
                
                # Health checks
                self._perform_health_checks()
                
                time.sleep(5)  # 5 second intervals
                
            except Exception as e:
                self.logger.error(f"Error in coordination loop: {e}")
                time.sleep(10)
    
    def _process_job_queue(self):
        """Process jobs in the queue."""
        while not self.job_queue.empty():
            try:
                priority, job_id = self.job_queue.get_nowait()
                job = self.active_jobs.get(job_id)
                
                if job and job.status == "pending":
                    asyncio.create_task(self._execute_job(job))
                    
            except Exception as e:
                self.logger.error(f"Error processing job queue: {e}")
                break
    
    async def _execute_job(self, job: TrainingJob):
        """Execute a training job."""
        try:
            job.status = "running"
            self.logger.info(f"Starting execution of job {job.job_id}")
            
            # Allocate resources
            allocated_nodes = await self.resource_manager.allocate_resources(job)
            
            # Execute based on strategy
            if job.strategy == TrainingStrategy.FEDERATED_LEARNING:
                await self._execute_federated_job(job, list(allocated_nodes.values()))
            elif job.strategy == TrainingStrategy.PARAMETER_SERVER:
                await self._execute_parameter_server_job(job, list(allocated_nodes.values()))
            elif job.strategy == TrainingStrategy.ALL_REDUCE:
                await self._execute_all_reduce_job(job, list(allocated_nodes.values()))
            else:
                await self._execute_generic_job(job, list(allocated_nodes.values()))
            
            # Release resources
            await self.resource_manager.release_resources(job.job_id, allocated_nodes)
            
            job.status = "completed"
            job.progress = 1.0
            self.job_history.append(job)
            
        except Exception as e:
            job.status = "failed"
            job.error_message = str(e)
            self.logger.error(f"Job {job.job_id} failed: {e}")
    
    async def _execute_federated_job(self, job: TrainingJob, nodes: List[WorkerNode]):
        """Execute federated learning job."""
        await self.federated_coordinator.start_federated_training(job, nodes)
    
    async def _execute_parameter_server_job(self, job: TrainingJob, nodes: List[WorkerNode]):
        """Execute parameter server training job."""
        # Implement parameter server logic
        self.logger.info(f"Executing parameter server job {job.job_id}")
        await asyncio.sleep(5)  # Simulate training
    
    async def _execute_all_reduce_job(self, job: TrainingJob, nodes: List[WorkerNode]):
        """Execute all-reduce training job."""
        # Implement all-reduce logic
        self.logger.info(f"Executing all-reduce job {job.job_id}")
        await asyncio.sleep(5)  # Simulate training
    
    async def _execute_generic_job(self, job: TrainingJob, nodes: List[WorkerNode]):
        """Execute generic training job."""
        self.logger.info(f"Executing generic job {job.job_id}")
        await asyncio.sleep(5)  # Simulate training
    
    def _update_node_heartbeats(self):
        """Update node heartbeats and check for stale nodes."""
        current_time = datetime.now()
        stale_threshold = timedelta(minutes=5)
        
        stale_nodes = []
        for node_id, node in self.resource_manager.nodes.items():
            if current_time - node.last_heartbeat > stale_threshold:
                stale_nodes.append(node_id)
                node.status = "offline"
        
        # Remove stale nodes
        for node_id in stale_nodes:
            del self.resource_manager.nodes[node_id]
            self.logger.warning(f"Removed stale node {node_id}")
    
    def _perform_health_checks(self):
        """Perform health checks on active nodes."""
        for node in self.resource_manager.nodes.values():
            if node.status == "busy":
                # Check if node is actually responsive
                # In a real implementation, this would ping the node
                pass
    
    async def _auto_scaling_loop(self):
        """Auto-scaling monitoring loop."""
        while self.running:
            try:
                scaling_result = await self.resource_manager.auto_scale()
                
                if scaling_result["scaling_action"]:
                    self.logger.info(f"Auto-scaling performed: {scaling_result}")
                
                # Record metrics
                self.metrics_collector.record_metric(
                    "cluster_load",
                    scaling_result["current_load"]
                )
                
                self.metrics_collector.record_metric(
                    "cluster_size",
                    scaling_result["node_count"]
                )
                
                await asyncio.sleep(60)  # Check every minute
                
            except Exception as e:
                self.logger.error(f"Auto-scaling error: {e}")
                await asyncio.sleep(60)
    
    async def submit_training_job(
        self,
        job_name: str,
        strategy: TrainingStrategy,
        model_config: Dict[str, Any],
        training_config: Dict[str, Any],
        data_config: Dict[str, Any],
        resource_requirements: Optional[Dict[ResourceType, int]] = None,
        priority: int = 1
    ) -> str:
        """
        Submit a new training job.
        
        Args:
            job_name: Human-readable job name
            strategy: Training strategy
            model_config: Model configuration
            training_config: Training parameters
            data_config: Data configuration
            resource_requirements: Required resources
            priority: Job priority (higher = more important)
            
        Returns:
            Job ID
        """
        job_id = f"job_{uuid.uuid4().hex[:8]}"
        
        job = TrainingJob(
            job_id=job_id,
            job_name=job_name,
            strategy=strategy,
            model_config=model_config,
            training_config=training_config,
            data_config=data_config,
            resource_requirements=resource_requirements or {ResourceType.CPU: 1},
            priority=priority
        )
        
        self.active_jobs[job_id] = job
        self.job_queue.put((-priority, job_id))  # Negative for max-heap behavior
        
        self.logger.info(f"Submitted training job {job_id}: {job_name}")
        return job_id
    
    async def get_job_status(self, job_id: str) -> Optional[Dict[str, Any]]:
        """Get status of a training job."""
        job = self.active_jobs.get(job_id)
        if not job:
            # Check history
            for historical_job in self.job_history:
                if historical_job.job_id == job_id:
                    job = historical_job
                    break
        
        if not job:
            return None
        
        return {
            "job_id": job.job_id,
            "job_name": job.job_name,
            "status": job.status,
            "progress": job.progress,
            "strategy": job.strategy.value,
            "created_at": job.created_at.isoformat(),
            "error_message": job.error_message,
            "result_location": job.result_location
        }
    
    async def cancel_job(self, job_id: str) -> bool:
        """Cancel a running or pending job."""
        job = self.active_jobs.get(job_id)
        if not job:
            return False
        
        if job.status in ["pending", "running"]:
            job.status = "cancelled"
            self.logger.info(f"Cancelled job {job_id}")
            return True
        
        return False
    
    async def get_cluster_status(self) -> Dict[str, Any]:
        """Get overall cluster status."""
        active_nodes = [node for node in self.resource_manager.nodes.values() if node.status != "offline"]
        busy_nodes = [node for node in active_nodes if node.status == "busy"]
        
        total_jobs = len(self.active_jobs)
        running_jobs = len([job for job in self.active_jobs.values() if job.status == "running"])
        pending_jobs = self.job_queue.qsize()
        
        return {
            "cluster_size": len(active_nodes),
            "busy_nodes": len(busy_nodes),
            "idle_nodes": len(active_nodes) - len(busy_nodes),
            "total_jobs": total_jobs,
            "running_jobs": running_jobs,
            "pending_jobs": pending_jobs,
            "completed_jobs": len(self.job_history),
            "ray_enabled": self.ray_initialized,
            "avg_cluster_load": self.resource_manager._calculate_cluster_load()
        }
    
    async def shutdown(self):
        """Shutdown the distributed training manager."""
        self.running = False
        
        if self.coordinator_thread:
            self.coordinator_thread.join(timeout=10)
        
        if self.ray_initialized:
            try:
                import ray
                ray.shutdown()
            except:
                pass
        
        self.logger.info("Advanced Distributed Training Manager shutdown complete")


# Factory function
def create_advanced_distributed_training_manager(config: Optional[ToxoConfig] = None) -> AdvancedDistributedTrainingManager:
    """Create and return an Advanced Distributed Training Manager instance."""
    return AdvancedDistributedTrainingManager(config) 